Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Embedded Computer Vision, exemplified by the migration from powerful workstations to embedded processors in computer vision applications, is a new and emerging field that enables an associated shift in application development and implementation. This comprehensive volume brings together a wealth of experiences from leading researchers in the field of embedded computer vision, from both academic and industrial research centers, and covers a broad range of challenges and trade-offs brought about by this paradigm shift. Part I provides an exposition of basic issues and applications in the area necessary for understanding the present and future work. Part II offers chapters based on the most recent research and results. Finally, the last part looks ahead, providing a sense of what major applications could be expected in the near future, describing challenges in mobile environments, video analytics, and automotive safety applications. Features: • Discusses the latest state-of-the-art techniques in embedded computer vision • Presents a thorough introductory section on hardware and architectures, design methodologies, and video analytics to aid the reader’s understanding through the following chapters • Offers emphasis on tackling important problems for society, safety, security, health, mobility, connectivity, and energy efficiency • Discusses evaluation of trade-offs required to design cost-effective systems for successful products • Explores the advantages of various architectures, development of high-level software frameworks and cost-effective algorithmic alternatives • Examines issues of implementation on fixed-point processors, presented through an example of an automotive safety application • Offers insights from leaders in the field on what future applications will be This book is a welcome collection of stand-alone articles, ideal for researchers, practitioners, and graduate students. It provides historical perspective, the latest research results, and a vision for future developments in the emerging field of embedded computer vision. Supplementary material can be found at http://www.embeddedvisioncentral.com.

Computer vision. --- Embedded computer systems. --- Embedded systems (Computer systems) --- Machine vision --- Vision, Computer --- Computer science. --- Computer graphics. --- Image processing. --- Pattern recognition. --- Computer Science. --- Image Processing and Computer Vision. --- Pattern Recognition. --- Computer Imaging, Vision, Pattern Recognition and Graphics. --- Computer systems --- Architecture Analysis and Design Language --- Artificial intelligence --- Image processing --- Pattern recognition systems --- Optical pattern recognition. --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Vision par ordinateur. --- Systèmes embarqués (informatique) --- Optical data processing. --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Optical computing --- Visual data processing --- Bionics --- Electronic data processing --- Integrated optics --- Photonics --- Computers --- Optical equipment

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

IbPRIA 2005 (Iberian Conference on Pattern Recognition and Image Analysis) was the second of a series of conferences jointly organized every two years by the Portuguese and Spanish Associations for Pattern Recognition (APRP, AERFAI), with the support of the International Association for Pattern Recognition (IAPR). This year, IbPRIA was hosted by the Institute for Systems and Robotics and the Geo-systems Center of the Instituto Superior Tecn ´ ico and it was held in Estoril, Por- gal. It provided the opportunity to bring together researchers from all over the world to discuss some of the most recent advances in pattern recognition and all areas of video, image and signal processing. There was a very positive response to the Call for Papers for IbPRIA 2005. We - ceived 292 full papers from 38 countries and 170 were accepted for presentation at the conference. The high quality of the scienti?c program of IbPRIA 2005 was due ?rst to the authors who submitted excellent contributions and second to the dedicated colla- ration of the international Program Committee and the other researchers who reviewed the papers. Each paper was reviewed by two reviewers, in a blind process. We would like to thank all the authors for submitting their contributions and for sharing their - search activities. We are particularly indebted to the Program Committee members and to all the reviewers for their precious evaluations, which permitted us to set up this publication.

Pattern recognition. --- Optical data processing. --- Artificial intelligence. --- Natural language processing (Computer science). --- Computer graphics. --- Pattern Recognition. --- Image Processing and Computer Vision. --- Artificial Intelligence. --- Natural Language Processing (NLP). --- Computer Graphics.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The computational paradigm considered here is a conceptual, theoretical and formal framework situated above machines and living creatures (two instant- tions), su?ciently solid, and still non-exclusive, that allows us: 1. tohelpneuroscientiststoformulateintentions,questions,experiments,me- ods and explanation mechanisms assuming that neural circuits are the p- chological support of calculus; 2. to help scientists and engineers from the ?elds of arti?cial intelligence (AI) and knowledge engineering (KE) to model, formalize and program the c- putable part of human knowledge; 3. to establish an interaction framework between natural system computation (NSC) and arti?cial system computation (ASC) in both directions, from ASC to NSC (in computational neuroscience), and from NSC to ASC (in bioinspired computation). With these global purposes, we organized IWINAC 2005, the 1st International Work Conference on the Interplay Between Natural and Arti?cial Computation, whichtookplaceinLasPalmasdeGranCanaria,CanaryIslands(Spain),during June 15-18, 2005, trying to contribute to both directions of the interplay: I: From Arti?cial to Natural Computation. What can computation, arti?cial intelligence (AI) and knowledge engineering (KE) contribute to the und- standing of the nervous system, cognitive processes and social behavior? This is the scope of computational neuroscience and cognition, which uses the computational paradigm to model and improve our understanding of natural science.

Computers. --- Algorithms. --- Artificial intelligence. --- Optical data processing. --- Pattern recognition. --- Evolutionary biology. --- Computation by Abstract Devices. --- Algorithm Analysis and Problem Complexity. --- Artificial Intelligence. --- Image Processing and Computer Vision. --- Pattern Recognition. --- Evolutionary Biology.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The three volume set LNCS 3496/3497/3498 constitutes the refereed proceedings of the Second International Symposium on Neural Networks, ISNN 2005, held in Chongqing, China in May/June 2005. The 483 revised papers presented were carefully reviewed and selected from 1.425 submissions. The papers are organized in topical sections on theoretical analysis, model design, learning methods, optimization methods, kernel methods, component analysis, pattern analysis, systems modeling, signal processing, image processing, financial analysis, control systems, robotic systems, telecommunication networks, incidence detection, fault diagnosis, power systems, biomedical applications, industrial applications, and other applications.

Computer science. --- Computer Communication Networks. --- Computer software. --- Computational complexity. --- Artificial intelligence. --- Optical pattern recognition. --- Computation by Abstract Devices. --- Computer Communication Networks. --- Algorithm Analysis and Problem Complexity. --- Discrete Mathematics in Computer Science. --- Artificial Intelligence. --- Pattern Recognition.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Statistical models of shape, learnt from a set of examples, are a widely-used tool in image interpretation and shape analysis. Integral to this learning process is the establishment of a dense groupwise correspondence across the set of training examples. This book gives a comprehensive and up-to-date account of the optimisation approach to shape correspondence, and the question of evaluating the quality of the resulting model in the absence of ground-truth data. It begins with a complete account of the basics of statistical shape models, for both finite and infinite-dimensional representations of shape, and includes linear, non-linear, and kernel-based approaches to modelling distributions of shapes. The optimisation approach is then developed, with a detailed discussion of the various objective functions available for establishing correspondence, and a particular focus on the Minimum Description Length approach. Various methods for the manipulation of correspondence for shape curves and surfaces are dealt with in detail, including recent advances such as the application of fluid-based methods. This complete and self-contained account of the subject area brings together results from a fifteen-year program of research and development. It includes proofs of many of the basic results, as well as mathematical appendices covering areas which may not be totally familiar to some readers. Comprehensive implementation details are also included, along with extensive pseudo-code for the main algorithms. Graduate students, researchers, teachers, and professionals involved in either the development or the usage of statistical shape models will find this an essential resource.

Computer Science. --- Pattern Recognition. --- Computer Imaging, Vision, Pattern Recognition and Graphics. --- Image Processing and Computer Vision. --- Computer science. --- Computer vision. --- Optical pattern recognition. --- Informatique --- Vision par ordinateur --- Reconnaissance optique des formes (Informatique) --- Mathematical optimization. --- Shape theory (Topology) --Statistical methods. --- Shape theory (Topology) --- Mathematical optimization --- Geometry --- Mathematics --- Physical Sciences & Mathematics --- Statistical methods --- Statistical methods. --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Computer graphics. --- Image processing. --- Pattern recognition. --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis --- Homotopy theory --- Mappings (Mathematics) --- Topological manifolds --- Topological spaces --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Machine vision --- Vision, Computer --- Artificial intelligence --- Image processing --- Pattern recognition systems --- Optical data processing. --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Optical computing --- Visual data processing --- Bionics --- Electronic data processing --- Integrated optics --- Photonics --- Computers --- Optical equipment